The Properties and Injector Nozzle Fouling Performance of Neat GTL and GTL/EN590 Diesel Blends in Various Diesel Engines
Bibliographic record
Abstract
The combination of high crude oil prices, energy security concerns and environmental drivers have resulted in an increased focus on alternative fuels. Gas to liquids (GTL) diesel is considered to be a promising alternative diesel fuel, given that it can be used directly as a diesel fuel or be blended with petroleum-derived diesel or biodiesel. GTL diesel fuels are predominantly paraffinic and possess several excellent inherent properties including virtually zero sulfur, very low aromatics (≺1%) and very high cetane values (typically ≻75). Currently GTL diesel is mostly sold into the European market as a blend stock for the extending and upgrading of petroleum-derived diesel fuels. Given GTL diesel's inherent paraffinic nature, the density of this product is below the European minimum 820 kg/m₃ EN590 specification (at 15°C). Typically up to 20% (vol) GTL diesel can be blended into crude-oil-derived diesel in order to meet the minimum European density specification, although neat applications of GTL diesel have also received some attention in literature. This study presents some findings pertaining to injector fouling for neat and 20% (vol) GTL diesel blends on a variety of diesel engines. There is however, very little information in the open literature on the physical and injector fouling performance properties of higher binary blends of petroleum-derived and GTL diesel. Several markets do not have a minimum density specification (e.g., the USA, Canada, Uzbekistan), while several countries have winter density specifications lower than 820 kg/m₃, including countries like China, potentially enabling GTL diesel to be used at higher blending ratios and in its neat form. In the current study, neat low temperature Fischer Tropsch (LTFT) GTL diesel and EN590 diesel fuels, together with blends containing 10, 20, 30, 50, and 80 volume percent GTL, were analyzed for their physical and chemical properties. The injector fouling performances of these blends were assessed in the CEC F-23-01 XUD9 and Sasol common rail tests. Many of the physical properties of these blends were influenced positively by the addition of GTL diesel to the EN590 fuel. Overall, it was concluded that at low percentages, blends of GTL diesel with EN590 resulted in similar injector fouling performance in the CEC F-23-01 XUD9 and Sasol common rail tests. At higher GTL diesel contents, a significant injector fouling performance improvement was found.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".